Machine learning systems can be used to predict outcomes based on input data. For example, given a set of input data, a regression-based machine learning system can predict an outcome. The regression-based machine learning system will likely have been trained on much training data in order to generate its regression model. It will then predict the outcome based on the regression model.
One issue with current systems is, however, that those predicted outcomes appear without any indication of why a particular outcome has been predicted. For example, a regression-based machine learning system will simply output a predicted result, and provide no indication of why that outcome was predicted. When machine learning systems are used as decision-making systems, this lack of visibility into why the machine learning system has made its decisions can be an issue. For example, when the machine learning system makes a prediction, which is then used as part of a decision, that decision is made without knowing why the machine learning system predicted that particular outcome based on the input. When the prediction or subsequent decision is wrong, there is no way to trace back and assess why the prediction was made by the machine learning system.
The techniques herein overcome these issues.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.